gtfuhr

oscars = pd.DataFrame(data=[5, 1, 3, 0], columns=["oscars"])
print(oscars)

gtfuhr

import pandas as pd
import numpy as np

movie_data = pd.DataFrame(data=[["Gladiator",8.5,"Russell Crowe"], 
                       ["Pulp Fiction",8.9,"John Travolta"],
                       ["The Godfather",9.2,"Marlon Brando"]], 
                 columns=["movie","imdb_rating","starring"])

print(movie_data)

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profile.to_notebook_iframe()

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# Here is the function that generates the report using Pandas-Profiling
profile = ProfileReport(df, title='Graduate Admission', html={'style':{'full_width':True}})

# Hint! If you were using a large dataset, set the minimal named argument to True
# profile = ProfileReport(large_dataset, minimal=True)

# It is also recommended to open the report as a html file, in this way Jupyter-Notebook
# does not becames laggy because of the big Jupyter-Notebook cell
profile.to_file(output_file="largeDatasetProfileReport.html")

gtfuhr

# First, as we normally do, we are going to import pandas and numpy
import numpy as np
import pandas as pd

# Thats where we import the function that will generate the ProfileReport
from pandas_profiling import ProfileReport

# Loads the dataset with the admission probability of various students and their
# scores in different tests of knowledge
df = pd.read_csv("Admission_Predict_Ver1.1.csv", encoding = 'unicode_escape')